Storage medium and case search apparatus

ABSTRACT

A non-transitory computer-readable storage medium storing instructions causing a computer of a case search apparatus to: execute a case search using a dynamic image, and output at least one of a similar case image similar to the dynamic image and a case candidate related to the dynamic image.

CROSS-REFERENCE TO RELATED APPLICATIONS

The entire disclosure of Japanese Patent Application No. 2021-132708filed on Aug. 17, 2021 is incorporated herein by reference.

BACKGROUND Technical Field

The present invention relates to a storage medium and a case searchapparatus.

Description of Related Art

Conventionally, when searching for similar cases using a searchapparatus that searches for cases, the doctor first checks the patientinformation, such as the chief complaint and patient accompanyinginformation of the relevant patient, and objective information based onthe examination images taken. When the doctor inputs the patient'ssymptom and possible disease name as keywords into the search apparatus,the search apparatus performs a search based on the input keywords. Insuch a keyword search, there is a possibility that the cases retrievedwill vary depending on the type of keywords considered by the doctor andthe doctor's skill in deciphering symptoms.

In this connection, JP 2007-279942 A describes a similar case searchapparatus that performs machine learning of the feature amounts obtainedfrom the case image and the diagnosis result of the case image, andsearches for case images similar to the diagnosis target image on thebasis of the feature amounts obtained from the diagnosis target image.

However, the medical images that are the case images and the diagnosistarget image described in JP 2007-279942 A are still images. Since theamount of information contained in still images is less than thatcontained in dynamic images, which are moving images, searches usingstill images in search apparatus may be less accurate than searchesusing moving images.

SUMMARY

One or more embodiments of the present invention provide a technologicalimprovement over conventional storage mediums and case searchapparatuses. For example, one or more embodiments provide a storagemedium for instructions and a case search apparatus that can performmore accurate search, which provides a practical and technologicalimprovement over conventional storage mediums and case searchapparatuses.

According to an aspect of the present invention, a non-transitorycomputer-readable storage medium stores instructions causing a casesearch apparatus to execute a case search, the instructions causing acomputer of the case search apparatus to: execute a search using adynamic image, and output at least one of a similar case image similarto the dynamic image and a case candidate related to the dynamic image.

According to another aspect of the present invention, a case searchapparatus comprises a hardware processor that executes a search using adynamic image, and outputs at least one of a similar case image similarto the dynamic image and a case candidate related to the dynamic image.

BRIEF DESCRIPTION OF THE DRAWINGS

The advantages and features provided by one or more embodiments of theinvention will become more fully understood from the detaileddescription given hereinafter and the appended drawings which are givenby way of illustration only, and thus are not intended as a definitionof the limits of the present invention, and wherein:

FIG. 1 is a view showing the entire configuration of a case searchsystem in one or more embodiments of the present invention;

FIG. 2 is a flowchart showing imaging control processing executed by acontroller of an imaging console in FIG. 1 ;

FIG. 3 is a flowchart showing case learning processing executed by acontroller of a diagnostic console in FIG. 1 ;

FIG. 4 is a view for explaining a method of dividing a dynamic imageinto multiple frame image groups;

FIG. 5 is a flowchart showing case search processing executed by thecontroller of the diagnostic console in FIG. 1 ;

FIG. 6 is a view showing an example of a search screen displayed on adisplay of the diagnostic console in FIG. 1 ; and

FIG. 7 is a flowchart showing case search processing executed by thecontroller of the diagnostic console in FIG. 1 in a modificationexample.

DETAILED DESCRIPTION OF EMBODIMENTS

Hereinafter, embodiments according to the present invention will bedescribed with reference to the drawings. However, the scope of theinvention is not limited to the disclosed embodiments or illustratedexamples.

[Configuration of Case Search System 100]

FIG. 1 shows the entire configuration of a case search system 100 in oneor more embodiments.

As shown in FIG. 1 , the case search system 100 is configured byconnecting an imaging apparatus 1 to an imaging console 2 via acommunication cable and such like, and connecting the imaging console 2to a diagnostic console 3 as a case search apparatus via a communicationnetwork NT such as a LAN (Local Area Network). The apparatuses formingthe case search system 100 are compliant with the DICOM (Digital Imageand Communications in Medicine) standard, and the apparatuses arecommunicated with each other according to the DICOM.

[Configuration of Imaging Apparatus 1]

The imaging apparatus 1 performs imaging of a dynamic state in a subjectwhich has cyclicity, such as the state change of inflation and deflationof a lung according to the respiration movement and the heartbeat, forexample. The dynamic imaging means obtaining a plurality of images byrepeatedly emitting a pulsed radiation such as X-ray to a subject at apredetermined time interval (pulse irradiation) or continuously emittingthe radiation (continuous irradiation) at a low dose rate withoutinterruption. In other words, dynamic imaging is the continuousradiographic imaging of the dynamic state of a target site havingcyclicity along a time axis. Dynamic imaging may be performed usingultrasound or magnetism as well as radiation such as X-rays. Dynamicimaging includes imaging of moving image, but does not include takingstill pictures while displaying a moving image.

A series of images obtained by the dynamic imaging is referred to as adynamic image.

The dynamic image can be obtained by imaging using a semiconductor imagesensor such as an FPD (Flat Panel Detector), for example.

The dynamic image includes a moving image, but does not include imagesobtained by imaging of still images while displaying a moving image.

Each of the plurality of images forming the dynamic image is referred toas a frame image. Hereinafter, embodiments will be described by taking,as an example, the dynamic imaging which is performed by the pulseirradiation. Though the following embodiments will be described bytaking, as an example, the subject M which is a chest of a patient beingtested, the present invention is not limited to this.

A radiation source 11 is located at a position facing a radiationdetection unit 13 through a subject M, and emits radiation (X ray) tothe subject M in accordance with control of an irradiation controlapparatus 12.

The irradiation control apparatus 12 is connected to the imaging console2, and performs radiographic imaging by controlling the radiation source11 on the basis of an irradiation condition which was input from theimaging console 2. The irradiation condition input from the imagingconsole 2 is a pulse rate, a pulse width, a pulse interval, the numberof imaging frames per imaging, a value of X-ray tube current, a value ofX-ray tube voltage and a type of applied filter, for example. The pulserate is the number of irradiation per second and consistent with anafter-mentioned frame rate. The pulse width is an irradiation timerequired for one irradiation. The pulse interval is a time from start ofone irradiation to start of next irradiation, and consistent with anafter-mentioned frame interval.

The radiation detection unit 13 is configured by including asemiconductor image sensor such as an FPD. The FPD has a glasssubstrate, for example, and a plurality of detection elements (pixels)is arranged in matrix at a predetermined position on the substrate todetect, according to the intensity, radiation which was emitted from theradiation source 11 and has transmitted through at least the subject M,and convert the detected radiation into electric signals to beaccumulated. Each pixel is formed of a switching section such as a TFT(Thin Film Transistor), for example. The FPD may be an indirectconversion type which converts X ray into an electrical signal byphotoelectric conversion element via a scintillator, or may be a directconversion type which directly converts X ray into an electrical signal.In one or more embodiments, a pixel value (signal value) of image datagenerated in the radiation detection unit 13 is a density value andhigher as the transmission amount of the radiation is larger.

The radiation detection unit 13 is provided to face the radiation source11 via the subject M.

The reading control apparatus 14 is connected to the imaging console 2.The reading control apparatus 14 controls the switching sections ofrespective pixels in the radiation detection unit 13 on the basis of animage reading condition input from the imaging console 2, switches thereading of electric signals accumulated in the pixels, and reads out theelectric signals accumulated in the radiation detection unit 13 toobtain image data. The image data is a frame image. The reading controlapparatus 14 outputs the obtained frame image to the imaging console 2.The image reading condition is, for example, a frame rate, frameinterval, a pixel size and an image size (matrix size). The frame rateis the number of frame images obtained per second and consistent withthe pulse rate. The frame interval is a time from start of obtaining oneframe image to start of obtaining the next frame image, and consistentwith the pulse interval.

Here, the irradiation control apparatus 12 and the reading controlapparatus 14 are connected to each other, and transmit synchronizingsignals to each other to synchronize the irradiation operation with theimage reading operation.

[Configuration of Imaging Console 2]

The imaging console 2 outputs the irradiation condition and the imagereading condition to the imaging apparatus 1, controls the radiographicimaging and reading operation of radiation images by the imagingapparatus 1, and displays the dynamic image obtained by the imagingapparatus 1 so that an imaging operator who performs the imaging such asan imaging operator confirms the positioning and whether the image isappropriate for diagnosis.

As shown in FIG. 1 , the imaging console 2 is configured by including acontroller 21, a storage 22, an operation unit 23, a display 24 and acommunication unit 25, which are connected to each other via a bus 26.

The controller 21 is configured by including a CPU (Central ProcessingUnit), a RAM (Random Access Memory) and such like. According to theoperation of the operation unit 23, the CPU of the controller 21 readsout instructions (e.g., system programs and various processing programs)stored in the storage 22 to load the instructions into the RAM, executesvarious types of processing including after-mentioned imaging controlprocessing in accordance with the loaded instructions, and integrallycontrols the operations of the sections in the imaging console 2 and theirradiation operation and reading operation of the imaging apparatus 1.

The storage 22 is configured by including a non-volatile semiconductormemory, a hard disk or the like. The storage 22 stores variousinstructions executed by the controller 21, parameters necessary forexecuting processing by the instructions, and data of processingresults. For example, the storage 22 stores instructions for executingthe imaging control processing shown in FIG. 2 . The storage 22 storesthe irradiation condition and the image reading condition so as to beassociated with the imaging site. The various instructions may be storedin a form of readable program code, and the controller 21 executes theoperations according to the program code as needed.

The operation unit 23 is configured by including a keyboard includingcursor keys, numeric keys and various function keys and a pointingdevice such as a mouse. The operation unit 23 outputs an instructionsignal input by a key operation to the keyboard or a mouse operation tothe controller 21. The operation unit 23 may include a touch panel onthe display screen of the display 24. In this case, the operation unit23 outputs the instruction signal which is input via the touch panel tothe controller 21.

The display 24 is configured by a monitor such as an LCD (Liquid CrystalDisplay) and a CRT (Cathode Ray Tube), and displays instructions inputfrom the operation unit 23, data and such like in accordance with aninstruction of a display signal input from the controller 21.

The communication unit 25 includes a LAN adapter, a modem, a TA(Terminal Adapter) and such like, and controls the data transmission andreception with the apparatuses connected to the communication networkNT.

[Configuration of Diagnostic Console 3]

The diagnostic console 3 obtains the dynamic image from the imagingconsole 2. The diagnostic console 3 displays the obtained dynamic image,generates a dynamic state analysis image by analyzing the obtaineddynamic image, and searches for and outputs similar case images similarto the dynamic image or case candidates related to the dynamic image onthe basis of the obtained dynamic image. Thus, the diagnostic console 3is a case search apparatus that supports diagnosis by the doctor.

As shown in FIG. 1 , the diagnostic console 3 is configured by includinga controller 31 (hardware processor), a storage 32, an operation unit33, a display 34 and a communication unit 35, which are connected toeach other via a bus 36.

The controller 31 is configured by including a CPU, a RAM and such like.According to the operation of the operation unit 33, the CPU of thecontroller 31 reads out the instructions (e.g., system programs storedin the storage 32 and various processing programs) to load them into theRAM and executes the various types of processing in accordance with theloaded instructions. The CPU of the controller 31 reads out theinstructions, e.g., a program 32 a stored in the storage 32 to load theprogram 32 a into the RAM, and executes after-mentioned case learningprocessing and case search processing according to the loaded program 32a.

The controller 31 searches by using the dynamic image, and outputssimilar case images that are similar to the dynamic image or casecandidates related to the dynamic image. The controller 31 functions asa controller.

The storage 32 is configured by including a nonvolatile semiconductormemory, a hard disk or the like. The storage 32 stores variousinstructions including the program 32 a for executing the case learningprocessing and the case search processing by the controller 31,parameters necessary for executing processing by the instructions anddata of processing results or the like. The various instructions may bestored in a form of readable program code, and the controller 31executes the operations according to the program code as needed.

The storage 32 also stores a dynamic image which was obtained by dynamicimaging in the past or a dynamic state analysis image obtained byanalyzing the dynamic image so as to be associated with anidentification ID for identifying the dynamic image or the dynamic stateanalysis image, patient basic information, patient accompanyinginformation, examination information, information on an image featurefocused in the diagnosis, diagnosis result including the disease name,medical record information (chief complaint, objective information,etc.), medical history, label information for bookmark/conference, andetc. The diagnosis result is the result of diagnosing the dynamic imageor the dynamic state analysis image. For example, the diagnosis resultsinclude the information which was input by a doctor after diagnosing animage, the information of a definite diagnosis obtained by pathologicalexamination, and the result information of automatic analysis of amedical image using CAD or other methods.

The storage 32 also stores dynamic images or dynamic state analysisimages obtained by imaging of multiple patients.

The storage 32 may store the dynamic image or the dynamic state analysisimage obtained in the past so as to be associated with the informationon the region of interest which was focused by the doctor in thediagnosis.

The storage 32 also stores a frame image group for one cycle of thedynamic state that is included in the dynamic image or the dynamic stateanalysis image, so as to be associated with the feature amounts whichwere calculated on the basis of the information on the image featurefocused in the diagnosis from the frame image group, and the informationon the group which was decided by machine learning on the basis of thefeature amounts. The group mentioned here is a group obtained bydividing the frame image groups included in the dynamic image or thedynamic state analysis image into several groups on the basis of apredetermined criteria (e.g., feature amounts in the frame images).

Here, the information on image feature focused in the diagnosis isdescribed. When a doctor performs diagnosis on the basis of the dynamicimage or the dynamic state analysis image which was generated on thebasis of the dynamic image, if there is an image feature such as alonger expiratory time compared to an inspiratory time, a longerrespiratory time, less change in density and a bad movement of adiaphragm, for example, the doctor performs diagnosis focusing on theimage feature. Thus, when the controller 31 of the diagnostic console 3displays the dynamic image or the dynamic state analysis image thereofon the display 34, the diagnostic console 3 also displays a userinterface for inputting or specifying information on the image featurefocused on by the doctor. In the storage 32, there is stored theinformation on the image feature which was input or specified by theoperation unit 33 from the user interface so as to be associated withthe dynamic image.

In one or more embodiments, when the diagnosis target is ventilation, itis possible to input or specify, as the focused image feature, any of aratio (or difference) between an expiratory time and an inspiratorytime, a respiratory time, a density change amount, a movement amount ofa diaphragm, and an average change amount of a density or the movementamount of the diaphragm in expiration and inspiration. When thediagnosis target is a pulmonary blood flow, it is possible to input orspecify, as the focused image feature, a time of one cycle, a densitychange amount, an average change amount from a maximum to a minimum (orfrom a minimum to a maximum) of the density change in one cycle, andsuch like.

As the past dynamic image, there is stored a dynamic image formed of aframe image group for one cycle of the dynamic state which was used fordiagnosis.

The operation unit 33 is configured by including a keyboard includingcursor keys, numeric keys and various function keys and a pointingdevice such as a mouse, and outputs an instruction signal input by a keyoperation to the keyboard and a mouse operation to the controller 31.The operation unit 33 may include a touch panel on the display screen ofthe display 34. In this case, the operation unit 33 outputs aninstruction signal, which was input via the touch panel, to thecontroller 31.

The display 34 is configured by including a monitor such as an LCD and aCRT, and performs various displays in accordance with the instruction ofa display signal input from the controller 31.

The communication unit 35 includes a LAN adapter, a modem, a TA and suchlike, and controls data transmission and reception with the apparatusesconnected to the communication network NT.

The dynamic state analysis image which is generated by analyzing thedynamic image is described here.

The dynamic state analysis image is an image generated by performing ananalysis process to the dynamic image. The dynamic state analysis imageis, for example, a blood flow analysis image in which the dynamic stateof blood flow function is analyzed, a ventilation analysis image inwhich the dynamic state of ventilation function is analyzed, or anadhesion analysis image in which the dynamic state of adhesion isanalyzed.

The analysis process in the dynamic state analysis image includes, forexample, frequency filtering in the time direction. For example, if thediagnostic target is ventilation, a low-pass filtering process (e.g.,cutoff frequency of 0.85 Hz) is applied to the density changes in theframe image group in the temporal direction. The dynamic state analysisimage is generated by extracting the density changes caused byventilation. For example, if the diagnostic target is pulmonary bloodflow, a high-pass filtering process (e.g., cutoff frequency of 0.85 Hz)is applied to the frame image group in the temporal direction. Thedynamic state analysis image is generated by extracting the densitychanges caused by the pulmonary blood flow. A bandpass filter (e.g., lowcutoff frequency of 0.8 Hz, high cutoff frequency of 2.4 Hz) may be usedto filter the density changes in the frame image group to extract thedensity changes due to pulmonary blood flow.

The analysis process may be performed by associating pixels in the sameposition of respective frame images in the frame image group to eachother and applying frequency filtering in the time direction to eachpixel. Each frame image in the frame image group may be divided intosub-regions consisting of multiple pixels, the representative value(e.g., mean value, median value, etc.) of the density values for eachdivided sub-region may be calculated, the divided sub-regions may beassociated with each other between frame images (e.g., sub-regions atthe same pixel position may be associated), and frequency filterprocessing in the time direction may be applied for each sub-region.

For each pixel (or each sub-region) of the frame image group to whichthe analysis process has been performed, the representative value (e.g.,variance value) in the time direction may be obtained, and a singleimage with the obtained value as the pixel value may be generated as thedynamic state analysis image.

[Operation of Case Search System 100]

Next, the operation of the case search system 100 will be described.

(Operations of Imaging Apparatus 1 and Imaging Console 2)

First, imaging operation by the imaging apparatus 1 and the imagingconsole 2 will be described.

FIG. 2 shows imaging control processing executed by the controller 21 inthe imaging console 2. The imaging control processing is executed incooperation between the controller 21 and the instructions stored in thestorage 22.

First, the controller 21 receives input of patient basic information forthe patient being tested (patient name, height, weight, age, gender andsuch like) and examination information (imaging site (here, chest) andthe type of the diagnosis target (ventilation, pulmonary blood flow orthe like)) made by the imaging operator, via the operation unit 23 inthe imaging console 2 (step S1).

Next, the controller 21 reads out the irradiation condition from thestorage 22 and sets the irradiation condition in the irradiation controlapparatus 12, and reads out the image reading condition from the storage22 and sets the image reading condition in the reading control apparatus14 (step S2).

Next, the controller 21 determines whether or not an instruction ofirradiation by the imaging operator was made via the operation unit 23(step S3). The imaging operator locates the subject M between theradiation source 11 and the radiation detection unit 13, and performspositioning. The imaging operator instructs the patient being tested tobe at ease to lead into quiet breathing. The imaging operator may inducedeep breathing by instructing “breathe in, breathe out”, for example.When the diagnosis target is pulmonary blood flow, for example, theimaging operator may instruct the patient being tested to hold thebreath since the image feature is obtained more easily when the imagingis performed while the patient holds the breath. When the preparationfor imaging is completed, the imaging operator operates the operationunit 23 to input an irradiation instruction.

When the irradiation instruction is input from the operation unit 23 bythe imaging operator (step S3: YES), the controller 21 outputs theimaging start instruction to the irradiation control apparatus 12 andthe reading control apparatus 14, and starts the dynamic imaging (stepS4). That is, radiation is emitted by the radiation source 11 at thepulse interval set in the irradiation control apparatus 12, and frameimages are obtained by the radiation detection unit 13.

When the imaging is finished for a predetermined number of frames, thecontroller 21 outputs an instruction to end the imaging to theirradiation control apparatus 12 and the reading control apparatus 14,and stops the imaging operation. The imaging is performed to obtain thenumber of frame images which can capture at least one respiration cycle.

The controller 21 then stores the frame images obtained by the imagingin the storage 22 so as to be associated with respective numbers (framenumbers) indicating the imaging order (step S5).

Next, the controller 21 displays the frame images obtained by theimaging on the display 24 (step S6). The imaging operator confirmspositioning and such like by the displayed dynamic image, and determineswhether an image appropriate for diagnosis was acquired by the imaging(imaging was successful) or imaging needs to be performed again (imagingfailed).

The controller 21 receives the input that the imaging was successful orthat the imaging failed by the imaging operator via the operation unit23, and determines whether or not the determination result indicatingthat the imaging was successful was input (step S7).

If the controller 21 has determined that the determination resultindicating that the imaging was successful was input (step S7: YES), thecontroller 21 attaches, to each of a series of frame images obtained bythe dynamic imaging in step S4, information such as the identificationID for identifying the dynamic image, the patient basic information, theexamination information, the irradiation condition, the image readingcondition and the number (frame number) indicating the imaging order(for example, the information is written into a header region of theimage data in the DICOM format), and transmits it to the diagnosticconsole 3 via the communication unit 25 (step S8). Then, the controller31 ends the processing. The controller 31 of the diagnostic console 3receives the series of frame images obtained by dynamic imaging via thecommunication unit 35, and stores them in the storage 32.

On the other hand, if the controller 21 determines that thedetermination result indicating that the imaging failed was input (stepS7: NO), the controller 21 deletes the series of frame images stored inthe storage 22 (step S9), and ends the processing. In this case, theimaging needs to be performed again.

(Operation of Diagnostic Console 3)

The operation of the diagnostic console 3 will be described.

The case learning processing shown in FIG. 3 will be first described.

The controller 31 executes the case learning processing in cooperationwith the instructions, e.g., the program 32 a stored in the storage 32,to learn group classification with the disease name as the correctanswer for the dynamic image or the dynamic state analysis image storedin the storage 32.

The learning of group classification with the disease name as thecorrect answer for the dynamic image or the dynamic state analysis imageis not limited to the example shown in FIG. 3 , and may be performed byany other method.

Hereinafter, the flow of case learning processing will be described withreference to FIG. 3 .

The controller 31 obtains the dynamic image or the dynamic stateanalysis image used in learning from the storage 32 (step S11).

Next, the controller 31 divides the obtained dynamic image or thedynamic state analysis image into frame image groups for respectivecycles of the dynamic state (step S12).

In the division in step S12, for example, density change of the entireimage is used. For example, a representative value (for example, averagevalue, median value or the like) of the density values is calculated ineach frame image of the dynamic image, and as shown in FIG. 4 , awaveform of the density change is obtained by plotting the calculatedrepresentative values of the density values temporally (in the frameimage order). The waveform is divided at frame images corresponding tolocal values (local maximum or local minimum), and thereby the dynamicimage is divided into frame image groups for respective cycles of thedynamic state of the subject M. The dynamic image may be divided intoframe image groups for respective cycles of the dynamic state byextracting the target site (for example, lung field region) from thedynamic image and using the density change in the extracted region.

For example, when the diagnosis target is ventilation, the division maybe performed after the density change is subjected to low pass filterprocessing (for example, cutoff frequency is 0.85 Hz) in the timedirection. Thus, it is possible to remove the signal change of highfrequency caused by pulmonary blood flow and such like and accuratelyextract the density change caused by the ventilation.

For example, when the diagnosis target is pulmonary blood flow, thedivision may be performed after the density change is subjected to highpass filter processing (for example, cutoff frequency is 0.85 Hz) in thetime direction. Thus, it is possible to remove the signal change of lowfrequency caused by ventilation and such like and accurately extract thedensity change caused by the pulmonary blood flow. The density change bythe pulmonary blood flow may be extracted by using a band pass filter(for example, cutoff frequency of low range is 0.8 Hz and cutofffrequency of high range is 2.4 Hz).

When the diagnosis target is ventilation, the division into a pluralityof frame image groups may be performed by using the change in themovement amount of the diaphragm. For example, in each frame image ofthe dynamic image, the diaphragm is recognized, the y coordinate at aposition of an x coordinate on the recognized diaphragm is obtained, andthe distance between the obtained y coordinate and a y coordinate whichis a reference (for example, distance from the y coordinate at theresting expiration position or the distance between the obtained ycoordinate and the lung apex) is plotted temporally. Thereby, a waveformof the temporal change in the movement amount of the diaphragm isobtained and divided at frame images corresponding to the local values(local maximum or local minimum) to divide the dynamic image into frameimage groups (frame image groups 1 to n (n>1 and n is integer)) forrespective cycles of the dynamic state of the subject. Here, thehorizontal direction is referred to as x direction and the verticaldirection is referred to as y direction in each of the frame images.

As for recognition of the diaphragm, for example, a lung field region isrecognized from the frame image, and the outline of the lower section ofthe recognized lung field region can be recognized as the diaphragm. Thelung field region may be extracted by any method. For example, athreshold value is obtained by a discriminant analysis from histogram ofthe signal value for each pixel of the frame image to recognize the lungfield region, and the region having higher signals than the thresholdvalue is primarily extracted as a lung field region candidate. Then,edge detection is performed around the border of the lung field regioncandidate which was primarily extracted, and the points having largestedges in sub-regions around the border are extracted along the border toextract the border of the lung field region.

The controller 31 obtains, from the storage 32, information on the imagefeature which is stored so as to be associated with the dynamic image orthe dynamic state analysis image obtained in step S11. The controller 31calculates the feature amounts R1 to Rn of the image feature on thebasis of the obtained information on the image feature, for therespective frame image groups 1 to n that were divided in step S12 (stepS13). In calculating the feature amounts R1 to Rn of the image feature,various such as histogram, gray value, pixel mean, center of gravity,entropy, edge, and contrast in frame image groups 1 to n may be used.The amounts of change in multiple biological sites (graph of change overtime) obtained from the dynamic state analysis image may also be used.

As mentioned above, when the diagnosis target is ventilation, the imagefeature includes any of a ratio (or difference) between an expiratorytime and an inspiratory time, a respiratory time, a density changeamount, a movement amount of a diaphragm, and an average change amountof a density or the movement amount of the diaphragm in expiration andinspiration. When the diagnosis target is a pulmonary blood flow, theimage feature includes any of the time of one cycle, a density changeamount, an average change amount from a maximum to a minimum (or from aminimum to a maximum) of the density change in one cycle, and such like.

The feature amounts R1 to Rn of the above image feature can becalculated on the basis of the density changes or the movement amountsof the diaphragm in the frame image groups.

As for the ratio between an expiratory time and an inspiratory time, theexpiratory time is obtained by calculating the time it takes for thedensity or the movement amount of the diaphragm to go from local maximumto local minimum in the frame image group, the inspiratory time isobtained by calculating the time it takes for the density or themovement amount of the diaphragm to go from local minimum to localmaximum in the frame image group, and the ratio can be obtained bycalculating the value of ratio between the expiratory time and theinspiratory time. The respiratory time can be obtained by adding theexpiratory time and the inspiratory time.

The density change amount can be obtained by calculating the amplitudevalue of the density change in the frame image group.

The movement amount of diaphragm can be obtained by calculatingamplitude value of the movement amount of diaphragm in the frame imagegroup.

The time of one cycle of the pulmonary blood flow can be obtained bycalculating the time it takes for the density of the frame image groupto go from local maximum (local minimum) to the next local maximum(local minimum).

When the diagnosis target is ventilation, the feature amounts R1 to Rnmay be calculated after the density change of each frame image group issubjected to low pass filter processing (for example, cutoff frequencyis 0.85 Hz) in the time direction. By this, it is possible to remove thesignal change of high frequency caused by pulmonary blood flow and suchlike and accurately extract the density change caused by theventilation.

When the diagnosis target is pulmonary blood flow, the feature amountsR1 to Rn may be calculated after the density change of each frame imagegroup is subjected to high pass filter processing (for example, cutofffrequency is 0.85 Hz) in the time direction. By this, it is possible toremove the signal change of low frequency caused by ventilation and suchlike and accurately extract the density change caused by the pulmonaryblood flow. The density change by the pulmonary blood flow may beextracted by using a band pass filter (for example, cutoff frequency oflow range is 0.8 Hz and cutoff frequency of high range is 2.4 Hz).

After extracting the pulmonary region from each frame image, the densitychange is calculated by using the pixels within the region. Thereby, itis possible to more accurately calculate the feature amounts R1 to Rnrelated to ventilation and pulmonary blood flow.

The controller 31 determines whether or not the region of interest inthe dynamic image or the dynamic state analysis image is set in advance(step S14).

If the region of interest is not set (step S14; NO), the controller 31learns group classification by performing machine learning on the basisof the feature amounts R1 to Rn which were calculated in step S13 (stepS15). As for the machine learning in step S15, any known machinelearning model may be used.

The controller 31 then classifies the dynamic image or the dynamic stateanalysis image which was obtained in step S11, by group, on the basis ofthe group classification learned in step S15 (step S16).

The controller 31 stores the dynamic image or the dynamic state analysisimage which was classified in step S16 so as to be associated with theinformation on group classification, the feature amounts R1 to Rn, andother information, in storage 32 (step S17), and ends the processing.The above other information includes the identification ID which isstored so as to be associated with the dynamic image or the dynamicstate analysis image, the patient basic information, the patientaccompanying information, the examination information, the informationon the image feature focused in the diagnosis, the disease name, medicalrecord information (chief complaint, objective information, etc.),medical history, label information for bookmark/conference, and etc.

If the region of interest is set (step S14; YES), the controller 31calculates the feature amounts R1 r to Rnr of the image featurerespectively for the region of interest r in the frame image groups 1 ton (step S18).

The controller 31 learns group classification by performing machinelearning on the basis of the feature amounts R1 r to Rnr which werecalculated in step S18 (step S19). As for the machine learning in stepS19, any known machine learning model may be used similarly to step S15.

The controller 31 then classifies, by group, the dynamic image or thedynamic state analysis image which was obtained in step S11 on the basisof the group classification learned in step S19 (step S20).

The controller 31 stores the dynamic image or the dynamic state analysisimage which was classified in step S20 so as to be associated with theinformation on group classification, the feature amounts R1 r to Rnr,and other information, in the storage 32 (step S21), and ends theprocessing.

The controller 31 executes the above case learning processing by usingthe dynamic images or the dynamic state analysis images obtained byimaging of multiple patients. When the group classification is learnedfor the dynamic image or the dynamic state analysis image by a methodother than the above case learning processing, the dynamic images or thedynamic state analysis images obtained by imaging of multiple patientsare also used.

In the case learning processing, the dynamic images or the dynamic stateanalysis images are learned for model of the machine learning. However,the present invention is not limited to this. Still images may belearned in addition to the dynamic images or the dynamic state analysisimages.

As for the group in the group classification learned in steps S15, S19in the above case learning processing, there may be different groupingaccording to the feature amount. To be specific, there may be groupingby the disease name, and the grouping further classifying the diseaseinto Type I, II, III, IV, etc.

In the learning of group classification with the disease name as thecorrect answer, it is ideally desirable to preform classification sothat the dynamic images or the dynamic state analysis images associatedwith information on a single disease name belong to one group. However,the dynamic images or the dynamic state analysis images associated withthe information on one disease name may branch and belong to multiplegroups.

Next, the case search processing shown in FIG. 5 will be described.

When the search target image is selected by the user on the searchscreen 341 shown in FIG. 6 to be described later via the operation unit33 of the diagnostic console 3 and the instruction to execute the searchis made, the case search processing is executed by cooperation betweenthe controller 31 and the program 32 a stored in the storage 32. Thesearch target image is the dynamic image used for the search. To bespecific, the search target image is the image which is not yetdiagnosed and the user intends to diagnose, among the dynamic images orthe dynamic state analysis images stored in the storage 32.

When the user selects the search target image, the user may specify theregion of interest which is the region to be focused in the diagnosis onthe search screen 341.

Hereinafter, with reference to FIG. 5 , the flow of case searchprocessing will be described.

The controller 31 first obtains the dynamic image or the dynamic stateanalysis image which is the search target image selected by the user,from the storage 32 (step S31). The step S31 is the obtaining step(obtaining).

Next, the controller 31 divides the search target image which wasobtained in step s31 into frame image groups for respective cycles ofthe dynamic state (step S32). The division in step S32 may be performedsimilarly to step S12 in the above case learning processing.

The controller 31 calculates the feature amounts R1 to Rn of the imagefeature for the respective frame image groups 1 to n that were dividedin step S32 (step S33). Similarly to step S13 in the case learningprocessing, in calculating the feature amounts R1 to Rn of the imagefeature in step S33, various such as histogram, gray value, pixel mean,center of gravity, entropy, edge, and contrast in frame image groups 1to n may be used. The amount of change in multiple biological sites(graph of change over time) obtained from the dynamic state analysisimage may also be used.

The controller 31 determines whether or not the region of interest inthe search target image is set (step S34).

If the region of interest is not set (step S34; NO), the controller 31decides the group to which the search target image belongs by using, forexample, the machine learning model learned in the case learningprocessing, on the basis of the feature amounts R1 to Rn calculated instep S33 (step S35).

The controller 31 then compares the feature amount Rn calculated in stepS33 with the feature amount Ry of the frame image group y constitutingthe dynamic image or the dynamic state analysis image learned in thecase learning processing and belonging to the group decided in step S35.The controller 31 sets each dynamic image or dynamic state analysisimage consisting of the frame image group y corresponding to the featureamount Ry which has a close distance to the feature amount Rn in thefeature amount space, as a display candidate in the order of closerdistance, that is, in the order of similarity (step S36). The dynamicimage or the dynamic state analysis image belonging to the group decidedin step S35 is referred to as a similar case image which is similar tothe search target image.

The controller 31 obtains the diagnosis result which is stored so as tobe associated with the similar case image from the storage 32 as a casecandidate, and refers to the diagnosis result for the disease name. Thecontroller 31 then displays, on the display 34, the search target image,the similar case images, and the diagnosis results of the similar caseimages as the case candidates related to the search target image in theorder of similarity for each disease name for which the controller 31referred to the diagnosis result (step S37), and ends the processing.That is, the controller 31 outputs the similar case images similar tothe search target image (dynamic image) and associated with the casecandidate, and the case candidates related to the search target image(dynamic image).

The similar case image indicates the image which is output as the imageof the case similar to that of the search target image (dynamic image).The similarity may be determined from the similarity of image itself, orthe similarity may be determined from case information other than theimage.

The steps S36 and S37 are search steps (searching).

If the region of interest is set (step S34; YES), the controller 31calculates the feature amounts R1 r to Rnr of the image featurerespectively for the region of interest r in the frame image groups 1 ton (step S38). The steps S33 and S38 are the feature amount calculationsteps (feature amount calculating).

The controller 31 decides the group to which the search target imagebelongs by using, for example, the machine learning model learned in thecase learning processing, on the basis of the feature amounts R1 r toRnr calculated in step S38 (step S39).

The controller 31 then proceeds to step S36. In step S36, the controller31 compares the feature amount Rnr calculated in step S38 with thefeature amount Ryr of the region of interest r in the frame image groupy constituting the dynamic image or the dynamic state analysis imagelearned in the case learning processing and belonging to the groupdecided in step S39. The controller 31 sets each dynamic image ordynamic state analysis image consisting of the frame image group ycorresponding to the feature amount Ryr which has a close distance tothe feature amount Rnr in the feature amount space, as a displaycandidate in the order of closer distance, that is, in the order ofsimilarity. The dynamic image or the dynamic state analysis imagebelonging to the group decided in step S39 is referred to as a similarcase image which is similar to the search target image.

FIG. 6 shows an example of the search screen 341 which the controller 31displays on the display 34 of the diagnostic console 3.

In the example shown in FIG. 6 , the search target image selected by theuser is displayed in the field A.

When the region of interest in the search target image is set inadvance, the region is marked on the image by the region B. When theuser can specify the region of interest on the search screen 341, thespecified region may be displayed as the region B.

The button C is a search button, and the user can instruct to executethe search by pressing the button C.

In the field D, the similar case images 342 set as the displaycandidates in step S36 of the case search processing and their diagnosisresults 343 are displayed in the order of similarity for each diseasename. The user can refer to the similar case images associated with theinformation on the disease name for reference by selecting the diseasename via the operation unit 33, and can compare the similar case imageswith the search target image.

In the field E, the similar case image selected from the field D via theoperation unit 33 by the user is displayed.

In the field F, the diagnosis result of the similar case image displayedin the field E is displayed.

In the example shown in FIG. 6 , the diagnosis result of the similarcase image may not displayed in the field D, and only the similar caseimage may be displayed.

In the field D and the field F, there may be displayed not only thediagnosis result of each similar case image but also the identificationID, the patient basic information, the patient accompanying information,the examination information, the information on the image featurefocused in the diagnosis, the disease name, medical record information(chief complaint, objective information, etc.), medical history, labelinformation for bookmark/conference, and etc., which are associated withthe similar case image

In the field G, there may be displayed the identification ID, thepatient basic information, the patient accompanying information, theexamination information, the information on the image feature focused inthe diagnosis, the disease name, medical record information (chiefcomplaint, objective information, etc.), medical history, labelinformation for bookmark/conference, and etc., which are associated withthe search target image.

Modification Example

Hereinafter, a modification example will be described.

Since the configuration in the modification example and the operationsof the imaging apparatus 1 and the imaging console 2 are similar tothose described in one or more embodiments, the explanation is omittedand the operation of the diagnostic console 3 will be described.

Hereinafter, with reference to FIG. 7 , the flow of the case searchprocessing in the modification example will be described.

The controller 31 first performs the steps S41 to S45 similar to thesteps S31 to S35 in the case search processing in one or moreembodiments.

Next, the controller 31 extracts dynamic images or dynamic stateanalysis images from, for example, the dynamic images or the dynamicstate analysis images learned in the case learning processing, andbelonging to the group decided in step S45, on the basis of theinformation associated with the search target image or the informationassociated with the learned dynamic image or dynamic state analysisimage (step S46). To be specific, the controller 31 extracts the dynamicimage or the dynamic state analysis image associated with the samepatient basic information as the patient basic information associatedwith the search target image. The controller 31 extracts the dynamicimage or the dynamic state analysis image associated with theinformation according to the conditions on which the user wants tosearch. The information according to the conditions on which the userwants to search is, for example, the identification ID, the patientbasic information, the patient accompanying information, the examinationinformation, the information on the image feature focused in thediagnosis, the diagnosis result including the disease name, medicalrecord information (chief complaint, objective information, etc.),medical history, label information for bookmark/conference, etc. Theextracted dynamic image or the dynamic state analysis image is referredto as a similar case image.

The controller 31 then compares the feature amount Rn calculated in stepS43 with the feature amount Rz of the frame image group z constitutingthe dynamic image or the dynamic state analysis image extracted in stepS46. The controller 31 sets each dynamic image or dynamic state analysisimage consisting of the frame image group z corresponding to the featureamount Rz which has a close distance to the feature amount Rn in thefeature amount space, as a display candidate in the order of closerdistance, that is, in the order of similarity (step S47).

The controller 31 obtains the diagnosis result which is stored so as tobe associated with each similar case image from the storage 32 as a casecandidate, and refers to the diagnosis result for the disease name. Thecontroller 31 then displays, on the display 34, the search target image,the similar case images, and the diagnosis results of the similar caseimages as the case candidates related to the search target image in theorder of similarity for each disease name for which the diagnosis resultwas referred to (step S48), and ends the processing. That is, thecontroller 31 outputs the similar case images similar to the searchtarget image (dynamic image) and associated with the case candidate, andthe case candidates related to the search target image (dynamic image).

The similar case image indicates the image which is output as an imageof the case similar to that of the search target image (dynamic image).The similarity may be determined from the similarity of image itself, orthe similarity may be determined from case information other than theimage.

The steps S46 to S48 are search steps (searching).

If the region of interest is set (step S44; YES), the controller 31performs the steps S49 and S50 similar to those of steps S38 and S39 inthe case search processing in one or more embodiments.

By performing the step S46 of the case search processing in themodification example, it is possible to narrow down the similar caseimages as display candidates, which enables to shorten the processingtime in steps S47 and S48.

The search target image and the similar case image may consist of theframe image group (multiple frame images) forming the dynamic image, ormay consist of a single frame image. That is, search target image andthe similar case image may consist of at least one frame image formingthe dynamic image. When the search target image and the similar caseimage consist of a single frame image, it is possible to execute thecase search by using one frame image among the multiple frame imagesforming the dynamic image having a larger amount of information thanthat of the still image. Thus, it is possible to perform more accuratecase search. When the search target image and the similar case imageconsist of multiple frame images, it is possible to execute the casesearch by using multiple frame images among the multiple frame imagesforming the dynamic image having a larger amount of information thanthat of the still image. Thus, it is possible to perform even moreaccurate case search.

When the search target image and the similar case image consist of theframe image group (multiple frame images) forming the dynamic image, theframe image group is consecutive frame images.

The search target image and the similar case image may be the image of aregion of interest in one frame image forming the dynamic image. In thiscase, by performing the case search for the region of interest which wasset in the frame image, it is possible to perform more accurate casesearch.

The search target image and the similar case image may be only a part offrame images among the multiple frame images forming the dynamic image.In this case, it is possible to execute the case search by using a partof frame images among the multiple frame images forming the dynamicimage having a larger amount of information than that of the stillimage. Thus, it is possible to perform more accurate case search.

When the search target image and the similar case image consist of asingle frame image forming the dynamic image, the single frame image maybe selected by the user. This enables to execute the case search byusing the frame image which was determined to be most suitable by theuser, and thus it is possible to perform more accurate case search.

In the steps S37 and S48 of the case search processing, the searchtarget image, the similar case images, and the diagnosis results of thesimilar case images are displayed in the order of similarity for eachdisease name. However, the present invention is not limited to this.Only the search target image and the disease names of the similar caseimages may be displayed, or only the search target image and the similarcase images may be displayed.

That is, the controller 31 controls to display (output) the similar caseimage which is similar to the search target image and associated withthe case candidate or the disease name of the similar case image whichis the case candidate related to the search target image. At least oneof the similar case image and the case candidate related to the casetarget image may be output. That is, any one of the similar case imageand the case candidate related to the search target image may be outputor both of them may be output.

The controller 31 may output multiple similar case images or casecandidates related to the search target image, or only one similar caseimage or case candidate related to the search target image may beoutput. By outputting multiple similar case images or case candidatesrelated to the search target image, the doctor can refer to moreinformation to help make a diagnosis.

The controller 31 may display the search target image, the similar caseimages, and the diagnosis results of the similar case images in theorder of similarity without dividing them by the disease name.

Though the machine-learned model which is used for the case searchprocessing is described to be learned by the above case learningprocessing, the present invention is not limited to this. The casesearch processing may be performed by using external machine-learnedmodel.

There may be used, for the case search processing, the model obtained bylearning, additionally to the machine-learned model, the dynamic imagewhich was taken by the imaging control processing or the dynamic stateanalysis image which was generated on the basis of the dynamic image.The dynamic image or the dynamic state analysis image used in theadditional learning may be weighted more to be learned than the otherdynamic image or the dynamic state analysis image. The weighted dynamicimage or the dynamic state analysis image is learned in the machinelearning model so that the search frequency is high at the time of casesearch processing. The weighting is made, for example, when thediagnosis result made with reference to the output result of the casesearch processing is different from definitive diagnosis (medicalrecords and pathology) and additional learning is performed by using thedefinitive diagnosis, when additional learning is performed by using thediagnosis result by a specific doctor who are considered to be a greatdoctor, when additional learning is performed for the dynamic image orthe dynamic state analysis image for which multiple doctors requesteddetailed view a large number of times and its diagnosis result, as acase which can be easily mistaken, and the case is used to perform theadditional learning, and etc.

As for the region of interest which is set in advance in the dynamicimage or the dynamic state analysis image in one or more embodiments andthe modification example, a single region of interest may be set ormultiple regions of interest may be set in the frame image.

In step S36 of the case search processing, the dynamic image or thedynamic state analysis image belonging to the group decided in step S35was set as the similar case image which is similar to the search targetimage. In step S46 in the modification example of the case searchprocessing, the dynamic image or the dynamic state analysis image isextracted from the learned dynamic image or dynamic state analysis imagebelonging to the group decided in step S45 on the basis of predeterminedinformation, and the extracted dynamic image or the dynamic stateanalysis image was set as the similar case image. However, the presentinvention is not limited to this. Not only the dynamic image or thedynamic state analysis image belonging to the same group as the searchtarget image, but also the dynamic image or the dynamic state analysisimage which has a close distance of the feature amount and belonging tothe adjacent group may be set as the similar case image. This enables tosearch many dynamic images or the dynamic state analysis images whichhave similar image feature but are associated with the information ondifferent disease name.

In the case search processing, the dynamic image or the dynamic stateanalysis image selected by the user as the search target image may havethe clear lesion area in the image. By using the dynamic image or thedynamic state analysis image which has the clear legion area in the casesearch processing, it is possible to perform accurate search.

In one or more embodiments and the modification example, the dynamicimage is the image obtained by imaging using radiation, ultrasound,magnetism, etc. However, the dynamic image used in the case learningprocessing and the case search processing may be the image obtained byimaging using radiation. Since radiation is the most commonly usedprimary screening method for clinics and other facilities that performdynamic imaging, the volume of dynamic images from radiation taken inthe past is the largest. By the case search processing using the modelobtained by machine learning of the dynamic images by radiation taken inthe past, it is possible to perform more accurate case search.

Since the dynamic state analysis image used in one or more embodimentsand the modification example includes the result of analysis processingas described above, the dynamic state analysis image has a larger amountof information than the dynamic image. Thus, it is possible to performmore accurate search by executing the case search processing with thedynamic state analysis image.

The dynamic images or the dynamic state analysis images obtained byimaging of multiple patients are used in learning of machine learningmodel which has learned group classification and which is used in stepsS35 and S45 in the case search processing. Thus, in steps S37 and S48 ofthe case search processing, it is possible to display the similar caseimage obtained by imaging of patient different from the patient capturedin the search target image or the diagnosis result of the differentpatient as the case candidate related to the search target image. Thatis, since it is possible to output the image or diagnosis result ofdifferent patient as the search result, the doctor can refer to moreinformation.

In the case search processing, the similar case image is the dynamicimage or the dynamic state analysis image. However, the still image(still picture) may be displayed as the similar case image.

The diagnostic console 3 may have a function of allowing the user toselect the dynamic image or the dynamic state analysis image, the stillimage, or both of the dynamic image or the dynamic state analysis imageand the still image, as the similar case image which is output in thecase search processing.

As described above, the instructions for causing the case searchapparatus to perform case search cause the controller 31 (computer) ofthe diagnostic console 3 (case search apparatus) to perform search step(step S36, S37, S46 to S48) that is performing search by using thedynamic image and outputting the similar case image similar to thedynamic image or the case candidate related to the dynamic image.

Accordingly, by using the dynamic image having a larger amount ofinformation than that of the still image, it is possible to execute thecase search using more information, and thus perform more accurate casesearch.

The controller 31 of the diagnostic console 3 performs obtaining step(steps S31, S41) that is obtaining the dynamic image, and the featureamount calculation step (step S33, S38, S43, S49) that is calculatingthe feature amount of the first image from the dynamic image obtained bythe obtaining step. In the search step, the controller 31 performscontrol to output the similar case image similar to the dynamic image orthe case candidate related to the dynamic image on the basis of thefeature amount of first image (feature amount Rn, Rnr) which wascalculated in the feature amount calculation step and the feature amountof the second image (feature amount Ry, Ryr) which was calculated bylearning multiple frame images of the arbitrary dynamic image inadvance.

Accordingly, it is possible to perform more accurate case search on thebasis of the dynamic image which was taken in the past.

The dynamic image used in search in the case search executed by thecontroller 31 of the diagnostic console 3 is at least one frame imageamong multiple frame images forming the dynamic image.

Accordingly, by using the frame image forming the dynamic image whichhas a larger amount of information than that of the still image, it ispossible to perform more accurate case search.

The dynamic image used in search in the case search executed by thecontroller 31 of the diagnostic console 3 is the image of the region ofinterest in at least one frame image among multiple frame images formingthe dynamic image.

Accordingly, it is possible to perform more accurate case search for theregion of interest which was set in the frame image.

In the case search executed by the controller 31 of the diagnosticconsole 3, the region of interest is the region specified by the user.

Accordingly, it is possible to execute the case search for the region ofinterest which the user determined to be most suitable, and thus it ispossible to perform more accurate case search.

In the case search executed by the controller 31 of the diagnosticconsole 3, the dynamic image used in search is multiple frame imagesforming the dynamic image.

Accordingly, since the search target image is multiple frame images, itis possible to execute the case search by suing more information, andthus it is possible to perform more accurate case search.

In the case search executed by the controller 31 of the diagnosticconsole 3, the dynamic image used in search is a single frame imageforming the dynamic image.

Accordingly, since the case search can be performed by using one frameimage among multiple frame images forming the dynamic image which has alarger amount of information than that of the still image, it ispossible to perform more accurate case search.

In the case search executed by the controller 31 of the diagnosticconsole 3, the dynamic image used in search is the image of the regionof interest in one frame image forming the dynamic image.

Accordingly, it is possible to perform more accurate case search for theregion of interest which was set in the frame image.

In the case search executed by the controller 31 of the diagnosticconsole 3, the dynamic image used in search is only a part of themultiple frame images forming the dynamic image.

Accordingly, since the case search can be performed by using a part ofmultiple frame images forming the dynamic image which has a largeramount of information than that of the still image, it is possible toperform more accurate case search.

In the case search executed by the controller 31 of the diagnosticconsole 3, the dynamic image used in search is the image selected by theuser among multiple frame images forming the dynamic image.

Accordingly, it is possible to execute the case search for the imagewhich the user determined to be most suitable to be used in the search,and thus it is possible to perform more accurate case search.

In the case search executed by the controller 31 of the diagnosticconsole 3, the dynamic image used in search is consecutive frame imagesamong multiple frame images forming the dynamic image.

Accordingly, since the search target image is consecutive frame images,it is possible to execute the case search by using more information, andthus it is possible to perform more accurate case search.

In the case search executed by the controller 31 of the diagnosticconsole 3, the dynamic image is an image obtained by continuousradiographic imaging along a time axis of the dynamic state of thetarget site.

Accordingly, by using the dynamic image having a larger amount ofinformation that that of the still image, it is possible to execute thecase search by using more information, and thus it is possible toperform more accurate case search.

In the case search executed by the controller 31 of the diagnosticconsole 3, the dynamic image is an image obtained by continuousradiographic imaging along a time axis of the dynamic state of thetarget site having a cyclicity.

Accordingly, by using the dynamic image having a larger amount ofinformation that that of the still image, it is possible to execute thecase search by using more information, and thus it is possible toperform more accurate case search.

In the case search executed by the controller 31 of the diagnosticconsole 3, the dynamic image used in search is the dynamic stateanalysis image obtained by analyzing the dynamic image.

Accordingly, by using the dynamic state analysis image having a largeramount of information, it is possible to execute the case search byusing more information, and thus it is possible to perform more accuratecase search.

In the case search executed by the controller 31 of the diagnosticconsole 3, the dynamic state analysis image is any of a blood flowanalysis image in which the dynamic state of blood flow function isanalyzed, a ventilation analysis image in which the dynamic state ofventilation function is analyzed, and an adhesion analysis image inwhich the dynamic state of adhesion is analyzed.

Accordingly, by using the blood flow analysis image, the ventilationanalysis image, or the adhesion analysis image having a larger amount ofinformation, it is possible to execute the case search by using moreinformation, and thus it is possible to perform more accurate casesearch.

In the case search executed by the controller 31 of the diagnosticconsole 3, the similar case image is the dynamic image.

Accordingly, it is possible to obtain more information more informationas the case search result by outputting the dynamic image having alarger amount of information than that of the still image as the similarcase image.

In the case search executed by the controller 31 of the diagnosticconsole 3, the similar case image is at least one frame image amongmultiple frame images forming the dynamic image.

Accordingly, it is possible to obtain more information more informationas the case search result by outputting the frame images that form thedynamic image having a larger amount of information than that of thestill image as the similar case image.

In the case search executed by the controller 31 of the diagnosticconsole 3, the similar case image is an image obtained by continuousradiographic imaging along a time axis of the dynamic state of thetarget site.

Accordingly, it is possible to obtain more information as the casesearch result by outputting the dynamic image having a larger amount ofinformation than that of the still image as the similar case image.

In the case search executed by the controller 31 of the diagnosticconsole 3, the similar case image is an image obtained by continuousradiographic imaging along a time axis of the dynamic state of thetarget site having a cyclicity.

Accordingly, it is possible to obtain more information as the casesearch result by outputting the dynamic image having a larger amount ofinformation than that of the still image as the similar case image.

In the case search executed by the controller 31 of the diagnosticconsole 3, the similar case image is the dynamic state analysis imageobtained by analyzing the dynamic image.

Accordingly, by outputting the dynamic state analysis image having alarger amount of information as the similar case image, it is possibleto obtain more information as the case search result.

In the case search executed by the controller 31 of the diagnosticconsole 3, the dynamic state analysis image is any of a blood flowanalysis image in which the dynamic state of blood flow function isanalyzed, a ventilation analysis image in which the dynamic state ofventilation function is analyzed, and an adhesion analysis image inwhich the dynamic state of adhesion is analyzed.

Accordingly, by outputting the blood flow analysis image, theventilation analysis image, or the adhesion analysis image having alarger amount of information as the similar case image, it is possibleto obtain more information as the case search result.

In the case search executed by the controller 31 of the diagnosticconsole 3, in the search step, multiple similar case images similar tothe dynamic image or multiple case candidates related to the dynamicimage are output.

Accordingly, the doctor can refer to more information to help make adiagnosis.

In the case search executed by the controller 31 of the diagnosticconsole 3, in the search step, the similar case images similar to thedynamic image or the case candidates related to the dynamic image areoutput in the order of similarity.

Accordingly, it is possible to refer to the similar case image which ismore similar to the search target image.

In the case search executed by the controller 31 of the diagnosticconsole 3, in the search step, the similar case images similar to thedynamic image or the case candidates related to the dynamic image areoutput for each disease name.

Accordingly, it is possible to perform more accurate case search withthe disease name as a reference.

In the case search executed by the controller 31 of the diagnosticconsole 3, in the search step, the similar case images similar to thedynamic image or the case candidates related to the dynamic image areoutput for each disease name in the order of similarity.

Accordingly, it is possible to perform more accurate case search withthe disease name as a reference and refer to the similar case imagewhich is more similar to the search target image.

The above description is an example of a case search system according toone or more embodiments of the present invention, and the presentinvention is not limited to this.

For example, in the above embodiments, the present invention is appliedto the dynamic image of the chest as an example. However, the presentinvention is not limited to this. The present invention may be appliedto the dynamic image obtained by imaging of other sites.

The storage 32 of the diagnostic console 3 in the above embodiments andmodification example may store diagnosed images and diagnostic reportsdiagnosed by the imaging diagnostic physician or clinician in alldiseases. By this, it is possible to use the images and diagnosticreports when providing education to residents, students, etc.

The storage 32 of the diagnostic console 3 in the above embodiments andmodification example may store electronic medical books which is thepractical reference books in the electronic form for imaging diagnosticphysician. When the user fills out a diagnostic report, the electronicmedical book may be configured to be viewed by displaying saidelectronic medical book on the display 34. It is desirable to have aconfiguration that the user can perform keyword search in the electronicmedical book.

Moreover, for example, the above description discloses an example ofusing a hard disk, a semiconductor nonvolatile memory, etc. as acomputer readable medium of the instructions according to one or moreembodiments of the present invention, but the medium is not limited tothis example. As other computer readable medium, portable recordingmedia such as CD-ROM can be applied. Carrier wave is also applicable asa medium to provide the data of the instructions according to one ormore embodiments of the present invention via communication lines.

As for the other detailed configurations and the detailed operations ofthe apparatuses forming the case search system 100, modifications can bemade within the scope of the present invention.

According to one or more embodiments of the present invention, it ispossible to perform more accurate search.

Although the disclosure has been described with respect to only alimited number of embodiments, those skilled in the art, having benefitof this disclosure, will appreciate that various other embodiments maybe devised without departing from the scope of the present invention.Accordingly, the scope of the invention should be limited only by theattached claims.

What is claimed is:
 1. A non-transitory computer-readable storage mediumstoring instructions causing a computer of a case search apparatus to:execute a case search using a dynamic image, and output at least one ofa similar case image similar to the dynamic image and a case candidaterelated to the dynamic image.
 2. The non-transitory computer-readablestorage medium according to claim 1, wherein the instructions cause thecomputer to: obtain the dynamic image, calculate a feature amount of afirst image from the dynamic image, and output at least one of thesimilar case image and the case candidate based on the feature amount ofthe first image and a feature amount of a second image calculated inadvance by learning multiple frame images in an arbitrary dynamic image.3. The non-transitory computer-readable storage medium according toclaim 1, wherein the dynamic image used in the case search is at leastone frame image among multiple frame images in the dynamic image.
 4. Thenon-transitory computer-readable storage medium according to claim 3,wherein the dynamic image used in the case search is an image of aregion of interest in the at least one frame image among the multipleframe images in the dynamic image.
 5. The non-transitorycomputer-readable storage medium according to claim 4, wherein theregion of interest is a region specified by a user.
 6. Thenon-transitory computer-readable storage medium according to claim 3,wherein the dynamic image used in the case search is multiple frameimages in the dynamic image.
 7. The non-transitory computer-readablestorage medium according to claim 3, wherein the dynamic image used inthe case search is one frame image in the dynamic image.
 8. Thenon-transitory computer-readable storage medium according to claim 3,wherein the dynamic image used in the case search is an image of aregion of interest in one frame image in the dynamic image.
 9. Thenon-transitory computer-readable storage medium according to claim 3,wherein the dynamic image used in the case search is only a part of themultiple frame images in the dynamic image.
 10. The non-transitorycomputer-readable storage medium according to claim 3, wherein thedynamic image used in the case search is an image selected by a useramong the multiple frame images in the dynamic image.
 11. Thenon-transitory computer-readable storage medium according to claim 3,wherein the dynamic image used in the case search is consecutive frameimages among the multiple frame images in the dynamic image.
 12. Thenon-transitory computer-readable storage medium according to claim 2,wherein the dynamic image is an image obtained by continuousradiographic imaging along a time axis of a dynamic state of a targetsite.
 13. The non-transitory computer-readable storage medium accordingto claim 2, wherein the dynamic image is an image obtained by continuousradiographic imaging along a time axis of a dynamic state of a targetsite having a cyclicity.
 14. The non-transitory computer-readablestorage medium according to claim 2, wherein the dynamic image used inthe case search is a dynamic state analysis image in which the dynamicimage is analyzed.
 15. The non-transitory computer-readable storagemedium according to claim 14, wherein the dynamic state analysis imageis any one of: a blood flow analysis image in which a dynamic state of ablood flow function is analyzed; a ventilation analysis image in which adynamic state of a ventilation function is analyzed; and an adhesionanalysis image in which a dynamic state of adhesion is analyzed.
 16. Thenon-transitory computer-readable storage medium according to claim 1,wherein the similar case image is a dynamic image.
 17. Thenon-transitory computer-readable storage medium according to claim 16,wherein the similar case image is at least one frame image amongmultiple frame images in the dynamic image.
 18. The non-transitorycomputer-readable storage medium according to claim 16, wherein thesimilar case image is an image obtained by continuous radiographicimaging along a time axis of a dynamic state of a target site.
 19. Thenon-transitory computer-readable storage medium according to claim 16,wherein the similar case image is an image obtained by continuousradiographic imaging along a time axis of a dynamic state of a targetsite having a cyclicity.
 20. The non-transitory computer-readablestorage medium according to claim 16, wherein the similar case image isa dynamic state analysis image in which the dynamic image is analyzed.21. The non-transitory computer-readable storage medium according toclaim 20, wherein the dynamic state analysis image is any one of: ablood flow analysis image in which a dynamic state of a blood flowfunction is analyzed; a ventilation analysis image in which a dynamicstate of a ventilation function is analyzed; and an adhesion analysisimage in which a dynamic state of adhesion is analyzed.
 22. Thenon-transitory computer-readable storage medium according to claim 1,wherein the instructions cause the computer to output at least one of:multiple similar case images similar to the dynamic image, and multiplecase candidates related to the dynamic image.
 23. The non-transitorycomputer-readable storage medium according to claim 1, wherein theinstructions cause the computer to output at least one of the similarcase image and the case candidate in order of similarity.
 24. Thenon-transitory computer-readable storage medium according to claim 22,wherein the instructions cause the computer to output at least one ofthe similar case image and the case candidate for each disease name. 25.The non-transitory computer-readable storage medium according to claim24, wherein the instructions cause the computer to output at least oneof the similar case image and the case candidate in order of similarityfor each disease name.
 26. A case search apparatus comprising: ahardware processor that: executes a case search using a dynamic image,and outputs at least one of a similar case image similar to the dynamicimage and a case candidate related to the dynamic image.